Improving Robot Behavior Optimization by Combining User Preferences

Abstract (Excerpt)

Recently it has been demonstrated that collaboration between
automated algorithms and human users can be especially effective
in robot behavior optimization tasks. In particular, we
recently introduced a Fitness-based Search with Preference-based
Policy Learning (FS-PPL) approach, in which the algorithm
models the user based on her preferences and then uses
the model, along with the fitness function, to guide search.
However, so far only interaction between a single human user
and an evolutionary algorithm was considered. If multiple
users contribute preferences, the algorithm must determine
whether to model them separately or jointly. In this paper we
describe an algorithm in which one evolutionary algorithminteracts
with two users and determines the best way to model
them automatically. We test the algorithm with automated
substitutes for human users and show that it performs better
for two users working together than for the same users working
separately, thus demonstrating the potential for crowdsourcing
robot behavior optimization.